Amalgamated Framework for Retrieval of Two Dimensional Images

The World Wide Web has become a vital source for information. Search enginesavailable now a days are giving results based on individual user’s search which is user dependent. Here we are trying to refine the search results irrespective of user by considering the user feedback. The refinement results can have lot of advantages and user experience. We can reconstruct the search results with same search query and user feedback. Results are refined in such a way that they are displayed in the order of relevancy.Relevance feedback is a technique to improve the results of retrieval. It uses information from the result of an initial retrieval to modify the query in such a way that more relevant documents are fetched the second time. A user may specify which documents are relevant and which are not. Although relevance feedback (RF) is existing methodology, but it has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF.Our framework shows advantage over traditional RF mechanisms in the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images.